Vulnerabilidades

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, dataset create and update mass-assignment allows cross-workspace dataset takeover. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, CustomTemplate create and update mass-assignment allows cross-workspace template takeover. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, assistant create and update mass-assignment allows cross-workspace assistant takeover. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, all CRUD endpoints for OpenAI Assistants Vector Store have no authentication middleware and the route path /api/v1/openai-assistants-vector-store is not in WHITELIST_URLS. However, it is also not protected…

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, when credentials are fetched with a credentialName filter parameter, the encryptedData field is not stripped from the response. The code properly omits encryptedData when no filter is used but fails to do…

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, POST /api/v1/node-custom-function lacks route-level authorization, allowing any authenticated user or API key to submit arbitrary JavaScript to the Custom JS Function node. When E2B_APIKEY is not configured…

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the assistant update endpoint of FlowiseAI. The endpoint allows authenticated users to modify server-controlled properties such as workspaceId, createdDate, and…

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, the checkBasicAuth endpoint validates credentials in plaintext without rate limiting and with direct comparison. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the chatflow update endpoint of FlowiseAI. The endpoint allows clients to modify server-controlled properties such as deployed, isPublic, workspaceId, createdDate,…

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the tool update endpoint of FlowiseAI. The endpoint allows authenticated users to modify server-controlled properties such as workspaceId, createdDate, and…

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the variable update endpoint of FlowiseAI. The endpoint allows authenticated users to modify server-controlled properties such as workspaceId, createdDate, and…

A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digest_utils of the file mlflow/data/digest_utils.py of the component Dataset Digest Computation. This manipulation causes use of weak hash. It is possible to launch the attack on the local host. The attack is considered to have…

A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can…

MLflow 3.9.0 with basic-auth (`--app-name basic-auth`) fails to enforce authorization checks for multiple Gateway API 'list' endpoints. Specifically, the `BEFORE_REQUEST_HANDLERS` dictionary in `mlflow/server/auth/__init__.py` does not include entries for `ListGatewaySecretInfos`, `ListGatewayEndpoints`, and…

A bug in Apache Airflow's KubernetesExecutor caused JWT tokens used by worker pods to authenticate against the Execution API to be passed to the worker container as command-line arguments visible in the pod spec. An authenticated UI/API user with Kubernetes read-only access to the cluster (e.g. `pods/get` in the…

Apache Airflow's EmailOperator and the underlying `airflow.utils.email` helpers established SMTP STARTTLS connections without verifying the remote certificate when the deployment used `[email] smtp_starttls=True` without `[email] smtp_ssl`. An attacker positioned between the worker and the configured SMTP server…

A bug in Apache Airflow's auth manager logout handling left previously-issued JWT tokens valid after the user clicked logout in the UI: the logout flow for `FabAuthManager` and `KeycloakAuthManager` did not actually reach the underlying `revoke_token()` call, so the JWT remained accepted by the API server until its…

The Event Log detail endpoint `GET /api/v2/eventLogs/{event_log_id}` in Apache Airflow fetched audit-log rows directly by numeric ID after only the generic Audit Log permission check, while the collection endpoint `GET /api/v2/eventLogs` applied per-Dag scoping. An authenticated UI/API user with audit-log read…

Exploitation requires the attacker to already be an authenticated Airflow worker holding a valid Log-server JWT issued for at least one Dag. Apache Airflow's Log server authorized JWT tokens against Dag IDs by applying Python's `str.lstrip()` to the requested path segment when verifying the JWT's `sub` claim.…

Apache Airflow's scheduler-side deadline-reference decoder (`SerializedCustomReference.deserialize_reference`) imported and dispatched arbitrary class paths drawn from DAG-author-controlled serialized state without an allowlist or plugin-registry gate. A DAG author whose code reaches the scheduler — the default on…

A bug in Apache Airflow's rendered-template field handling caused nested sensitive-key masking (e.g. nested `password` / `token` / `secret` / `api_key` keys inside a JSON template structure) to be bypassed when the rendered field exceeded `[core] max_templated_field_length`: Airflow stringified the structure before…

A bug in Apache Airflow's XCom PATCH endpoint `PATCH /api/v2/xcomEntries/{key}` allowed an authenticated UI/API user with XCom write permission on a Dag to set XCom entries under reserved key names (e.g. `return_value`) that the matching POST endpoint already validated against `FORBIDDEN_XCOM_KEYS`. The endpoint also…

A bug in Apache Airflow's Variable response masker caused nested-key redaction (triggered by secret-suffixed key names like `password`, `token`, `secret`, `api_key`) to be bypassed when the JSON value's nesting depth exceeded the shared secrets masker's recursion limit: the masker returned the original nested item…

Apache Airflow's official documentation at `core-concepts/dag-run.html` ("Passing Parameters when triggering Dags") showed a verbatim `BashOperator(bash_command="echo value: {{ dag_run.conf['conf1'] }}")` example without any quoting / sanitization warning. Dag authors who copied the pattern verbatim into deployments…

A bug in Apache Airflow's bulk Task Instances API (`PATCH/DELETE /api/v2/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances`) evaluated authorization against the `dag_id` resolved from the URL path while operating on the `dag_id` / `dag_run_id` extracted from request-body entity fields. An authenticated UI/API user with…